class_docstr: Meter to calculate top-k precision for single-label or multi-label
  image classification task. Note, ties are resolved randomly.
class_docstr_tok: ["Meter","to","calculate","top-k","precision","for","single-label","or","multi-label","image","classification","task",".","Note",",","ties","are","resolved","randomly","."]
docstr: Contains the states of the meter.
docstr_tok: ["Contains","the","states","of","the","meter","."]
code: def get_classy_state(self):
      """Contains the states of the meter."""
      return {
          "name": self.name,
          "top_k": self._topk,
          "total_correct_predictions": self._total_correct_predictions_k.clone(),
          "total_sample_count": self._total_sample_count.clone(),
          "curr_sample_count": self._curr_sample_count.clone(),
          "curr_correct_predictions_k": self._curr_correct_predictions_k.clone(),
      }
  
file_path: ClassyVision/classy_vision/meters/precision_meter.py
imports: from typing import Any, Dict, Sequence
  import torch
  from classy_vision.generic.distributed_util import all_reduce_sum
  from classy_vision.generic.util import is_pos_int, maybe_convert_to_one_hot
  from classy_vision.meters import ClassyMeter
  from .None import register_meter
label_desc: Use image node_id 6 for calling the PrecisionAtKMeter obj's underlying member method code with example usage: obj.get_classy_state() and returns: dict